A Theological Engagement with the Science of Science Skepticism

When Christians reject the claims of scientific experts, are they being irrational? Much of recent discussion in scholarly and popular media have discussed science denialism by conservative Christians, linking a low view of scientific expertise to the United States’ current political turmoil. This paper will focus on scientific explanations of science skepticism, asking whether there is anything unique to religious communities that make them vulnerable to misinformation.

The science of science: Clinical Science launches a new translational meta-research collection

Clinical Science is proud to launch a new translational meta-research collection. Meta-research, or the science of science, applies the scientific method to study science itself. Meta-research is a powerful tool for identifying common problems in scientific papers, assessing their impact, and testing solutions to improve the transparency, rigor, trustworthiness, and usefulness of biomedical research. The collection welcomes science of science studies that link basic science to disease mechanisms, as well as meta-research articles highlighting opportunities to improve transparency, rigor, and reproducibility among the types of papers published in Clinical Science. Submissions might include science of science studies that explore factors linked to successful translation, or meta-research on experimental methods or study designs that are often used in translational research. We hope that this collection will encourage scientists to think critically about current practices and take advantage of opportunities to make their own research more transparent, rigorous, and reproducible.

Five science strategies for tackling socio-political COVID-19 fallout

On 31 December 2019, the World Health Organization was informed of a viral pneumonia that was 10 days later identified as a novel coronavirus. An unprecedented pandemic ensued and tackling the virus and managing its repercussions has since been a priority for researchers and policy makers worldwide. The virus has taken lives, jobs and homes have been lost, and ways of life have been completely upended. The virus has created challenges and caused destruction and with a view to aiding recovery, the United Nations (UN) has published its Framework for the Immediate Socio-Economic Response to COVID-19. This includes the UN Research Roadmap for the COVID-19 Recovery, which highlights how countries across the globe can work together to develop strategies informed by science and evidence. In response to the complexity of COVID-19, the roadmap comprises several interrelated strands, including a focus on science strategies to merge and unite research and information. The roadmap has identified five scientific strategies to ensure science can be effectively applied to key challenges. These are: data infrastructure, implementation science, rapid learning systems, knowledge mobilisation and science of science.

Science of science

BACKGROUND. The increasing availability of digital data on scholarly inputs and outputs – from research funding, productivity, and collaboration to paper citations and scientist mobility – offers unprecedented opportunities to explore the structure and evolution of science. The science of science (SciSci) offers a quantitative understanding of the interactions among scientific agents across diverse geographic and temporal scales: It provides insights into the conditions underlying creativity and the genesis of scientific discovery, with the ultimate goal of developing tools and policies that have the potential to accelerate science. In the past decade, SciSci has benefited from an influx of natural, computational, and social scientists who together have developed big data–based capabilities for empirical analysis and generative modeling that capture the unfolding of science, its institutions, and its workforce. The value proposition of SciSci is that with a deeper understanding of the factors that drive successful science, we can more effectively address environmental, societal, and technological problems.ADVANCES. Science can be described as a complex, self-organizing, and evolving network of scholars, projects, papers, and ideas. This representation has unveiled patterns characterizing the emergence of new scientific fields through the study of collaboration networks and the path of impactful discoveries through the study of citation networks. Microscopic models have traced the dynamics of citation accumulation, allowing us to predict the future impact of individual papers. SciSci has revealed choices and trade-offs that scientists face as they advance both their own careers and the scientific horizon. For example, measurements indicate that scholars are risk-averse, preferring to study topics related to their current expertise, which constrains the potential of future discoveries. Those willing to break this pattern engage in riskier careers but become more likely to make major breakthroughs. Overall, the highest-impact science is grounded in conventional combinations of prior work but features unusual combinations. Last, as the locus of research is shifting into teams, SciSci is increasingly focused on the impact of team research, finding that small teams tend to disrupt science and technology with new ideas drawing on older and less prevalent ones. In contrast, large teams tend to develop recent, popular ideas, obtaining high, but often short-lived, impact.OUTLOOK. SciSci offers a deep quantitative understanding of the relational structure between scientists, institutions, and ideas because it facilitates the identification of fundamental mechanisms responsible for scientific discovery. These interdisciplinary data-driven efforts complement contributions from related fields such as scientometrics and the economics and sociology of science. Although SciSci seeks long-standing universal laws and mechanisms that apply across various fields of science, a fundamental challenge going forward is accounting for undeniable differences in culture, habits, and preferences between different fields and countries. This variation makes some cross-domain insights difficult to appreciate and associated science policies difficult to implement. The differences among the questions, data, and skills specific to each discipline suggest that further insights can be gained from domain-specific SciSci studies, which model and identify opportunities adapted to the needs of individual research fields.Abstract. Identifying fundamental drivers of science and developing predictive models to capture its evolution are instrumental for the design of policies that can improve the scientific enterprise – for example, through enhanced career paths for scientists, better performance evaluation for organizations hosting research, discovery of novel effective funding vehicles, and even identification of promising regions along the scientific frontier. The science of science uses large-scale data on the production of science to search for universal and domainspecific patterns. Here, we review recent developments in this transdisciplinary field.

How to Understand a City via Mobility

Recent advancements in data science technologies have allowed researchers to utilize large-scale records of human mobility to study various topics from city growth models to tracing outbreaks and analyzing the labor market. In this paper, after introducing recent studies on human mobility using transportation data, we briefly review the existing studies by applying large-scale human mobility data to three different topics: epidemics, economics, and science of science. As the early attempts of interdisciplinary studies, these studies reveal how human mobility records can help us solve significant social, economic, and public health issues in our era.

Neural embeddings of scholarly periodicals reveal complex disciplinary organizations

Understanding the structure of knowledge domains is one of the foundational challenges in the science of science. Here, we propose a neural embedding technique that leverages the information contained in the citation network to obtain continuous vector representations of scientific periodicals. We demonstrate that our periodical embeddings encode nuanced relationships between periodicals and the complex disciplinary and interdisciplinary structure of science, allowing us to make cross-disciplinary analogies between periodicals. Furthermore, we show that the embeddings capture meaningful “axes” that encompass knowledge domains, such as an axis from “soft” to “hard” sciences or from “social” to “biological” sciences, which allow us to quantitatively ground periodicals on a given dimension. By offering novel quantification in the science of science, our framework may, in turn, facilitate the study of how knowledge is created and organized.